Book Image

SQL Server 2017 Machine Learning Services with R.

By : Julie Koesmarno, Tomaž Kaštrun
Book Image

SQL Server 2017 Machine Learning Services with R.

By: Julie Koesmarno, Tomaž Kaštrun

Overview of this book

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.
Table of Contents (12 chapters)

Analytical barriers

Many companies encounter barriers when trying to analyse their data. These barriers are usually knowledge scarcity (not all departments have the knowledge to analyse the data) and data dispersity (usually data arrives from different sources).

Enterprises divide responsibilities according to the roles or functions of the employees. Such division of work has a positive effect, especially when an enterprise is large. Usually, small to mid-sized enterprises adopt such roles as well, but they are normally granulated on a higher level due to a smaller number of employees.

With rapid market changes, the emergence of new technologies, and the need for faster adaptation, many experts have noticed many of the following barriers:

  • Data scarcity and data dispersity
  • Complex (and many times outdated) architecture
  • Lack of knowledge
  • Low productivity
  • Slow adaptation to market...